We prepared a manual for the annotation, such as the definition of trouble information, in advance.

for more correct and reliable annotation, we need to annotate TSs with several annotators and compute the agreement among them.


The system contains 61 components (value component, VC) covering 8 frames of personal values extracted from 20,000 questionnaires with principal component analysis (PCA).

an abbreviated and more flexible version of this system with 22 components (mini version) was preferred to use, because the questionnaire of mini version has only 60 questions as opposed to 303 questions for the full version.


Up to now, we have gathered 1,002 pieces of ef-  fective data for our "Weibo-Societas" Model.

we found that for Chinese people (especially who use Weibo), some personal values are very different from Japanese Twitter users' ones.


In contrast, a character repetition does not frequently occur in tweets during non-emotional upsurge.

to our knowledge, there has not been an attempt to capture emotional upsurge using the linguistic characteristics of tweets.


In many of the tasks of natural language processing, supervised learning has been a great success.

if we want to use a supervised learning for real problems, there is often problems in domain adaptation.


Therefore, in this paper, first, define the distance d(f ) between P s and P t as following: Then set the weight by using d(f ).


Generally SVM has a higher accuracy than NB.

nB sometimes has high accuracy in the case of document classification.


The problem with vector-based methods is to focus on the frequency of separate words or phrases.

plagiarists can paraphrase by replacing words with similar words that have a very different frequency.


The high paraphrase recall (93%) it attained shows that most paraphrases have the same predicate argument tuples.


The maximum similarity (MAXSIM) metric (Chan and Ng, 2008) finds the maximum matching of unigram, bigram, and trigram words by using the Kuhn-Munkres algorithm.

the maximum length of the phrase is a trigram.


The most common approach is usually based on feature extraction from all sentences in each review.

all sentences in a review do not always contribute to the prediction of a specific aspect in the review.


They estimated the aspect weights of each sentence directly in their model.

our method identifies the aspect of each sentence by using a machine learning method separately.


One of the earliest attempts at rating prediction that combines both review text and ratings is (Ganu et al., 2009).

their review segmentation method differs from ours in that their work depends on manual annotation of each review sentence into predetermined domain-specific aspects and the training of separate classifiers for each aspect.


(Wang et al., 2010) approach the problem of segmentation by measuring the overlap between each sentence of the review and the seed words describing each aspect.

these aspect seed words are chosen manually which are, again, domain-specific.


proposed a cross-lingual PRF model based on bilingual topics and showed better results on parallel or comparable corpus.

the hypothesis of common shared bilingual topics is not always suitable for unparallel documents, since they are often poor in content relevance.


The LDA-based PRF methods showed a slightly better performance than former methods, verifying the fact that a fine-grained topic may introduce more relevant terms into query expansion.

the PRF method based on bilingual LDA model, which used to achieve better performance than monolingual models on parallel documents, showed no advantage here, since the poor quality of the unparallel feedback documents limited the effectiveness of topical PRF methods.


Recognizing spatial information associated with events expressed in natural language text is essential for the proper interpretation of such events.

the associations between events and spatial information found throughout the text have been much less studied than other types of spatial association as looked into in SpatialML and ISO-Space.


The availability of the standard and corpus has promoted further studies on extracting temporal information associated with events from text (Lapata and Lascarides, 2006;Mani et al., 2006;Yoshikawa et al., 2009;Mirza and Tonelli, 2014), including the TempEval challenges (Verhagen et al., 2009;Verhagen et al., 2010;UzZaman et al., 2013).

analyzing spatial properties of events has received less attention than temporal analysis, though in recent years a few studies attempt to tackle relevant problems.


A range of verbs that exhibit changes in the state of the world usually belong to this category.

we do not restrict event mentions to a certain category of verbs.


Our pilot annotation suggests that noun or adverbial phrases are sufficient enough to represent the space associated with events in text.

we acknowledge one exception to this: adjectival forms of place names and their demonymic equivalents such as Canadian, South American, and Northern Irish can be annotated separately as a spatial expression even though they exist within a longer noun phrase, as shown in the example below.


At the same time, many experts and other knowledgeable people posted tweets to refute such false rumors as "There is no truth to the rumor that a nuclear explosion has occurred in Fukushima."

since many people did not notice such refutations, they retweeted the false rumors, inadvertently fueling the confusion and creating serious obstacles to rescue activities.


The association between social media and suicide has drawn public attention recently, since several actual suicidal cases were reported in Sina Weibo, e.g., (http://news.sina.com.cn/zl/ zatan/2014-12-02/18032759.shtml).

new approaches towards online suicide ideation monitoring and prevention are still under development.


According to (Kay et al., 1987), positive information would reduce the influence of negative emotion.

researchers (Martin et al., 1993) found that negative effects bring longer and deeper impacts than positive effects.


On one hand, these long-term (soft) monotonous movement will render prediction more difficult since public mood changes drastically.

prediction in longterm monotonous periods in less meaningful than it is in fluctuating periods for stock investors.


Besides, we can see that WDM works much better in fluctuating period than monotonous periods and achieves the best value when lag is 2 in fluctuating period.

both DM and Raw have little predictive ability in fluctuating period.


Holmberg's system thus suggests that the language with the truth-based system has only low negation.

in languages like Cantonese, Japanese, and Korean, there are surely examples where the negation is in high position, but still induces the truth-based system (Kim 2016).


It is not possible to identify any overt antecedent at syntax.

in our semantic/pragmatic-based system, the negative particle can mean that the speaker does affirm the negative value of the proposition such that the child touches the socket.


Information structures 1 such as definiteness and specificity affect the selection of articles.

some languages such as Japanese and Russian do not have articles for noun phrases (NPs).


The animacy (animate/inanimate) affected only FPT.

the sentience (sentient/insentient) affected FPT and RPT.


The previous co-reference resolution methods addressed the issue of the information status based on whether or not an NP is mentioned in the previous discourse.

these methods cannot identify the information structure for a recipient, such as the commonness (hearer-new, -old or bridging).


Indeed, it is worth noting that the responsibilities shared by pilots and ATCs must adhere to the use of standard phraseology.


While the Air Traffic Services (ATS) of the CAAP agreed to accommodate interviews with the pilots and ATCS, it could not provide or release copies of the conversation transcripts.

due to strong requisition of the study, the ATS released only three transcripts, ensuring that the airline companies remained anonymous.


Unweighted voting makes all base parsers give the same score for all dependency relations.

weighted voting makes base parsers with better accuracy give bigger score for particular dependency relations.


Thus, we propose the following lexical entries for two types of omow'think': It should be noted that in a raising verb (8a), an accusative-marked NP is located outside XP against the Exceptional Case-marking (ECM) analysis (Kaneko, 1988;Ueda, 1988;Hiraiwa, 2001;Taguchi, 2009) in which such an NP is located inside XP as shown in 9a In (9b), when Naomi is marked with ga, the complement clause XP containing it can be clefted.

when Naomi is marked with o, the clause can not be clefted although the accusativemarked NP is expected to be occupied within XP.


Our work employs the same technologies as previous works.

we extend MDL-based monolingual model to bilingual.


The previous section introduced that we use the ∆DL to evaluate changes of possible candidates on description length.

the order of applying a new alignment unit is also very important.


Kytea (monolingual segmentation method) have different granularity of the segmentation in Chinese and Japanese.

bilingual MDL-based method share similar granularity across both languages.


Words segmented by Kytea have small granularity.

our method (MDL-based segmentation) have smoother distribution and larger segmentation granularity.


Due to our method is initial step of NLP task, in this experiment we use translation accuracy of building SMT system as evaluation of our method.

we also suggest that our segmentation method could be evaluated with other machine translation system.


On the one hand, in the phrase-based model, the lexical reordering model is a crucial component, but it is often be criticized, especially when translating a language pair with widely divergent syntax like English-Japanese, as the naïve distance-based lexical reordering model does not work so well when applied to longer reorderings.

in syntax-based SMT method, word reordering is implicitly addressed by translation rules.


In recent years, the recurrent neural network based language models have defeated the conventional n-gram based techniques.

it is difficult for neural network architectures to use linguistic annotations.


Referring to this phenomenon, especially if we focus on the realm of interrogation for solving crimes, it is a compelling matter when people are challenged to be able to tell which utterances contain lies.

for many people, it seems difficult to recognize any deception, considering that the cues to deception can be reflected from diverse aspects (DePaulo et al., 2003) as well as the need for specific experience in related scientific fields.


Due to the unavailability of Indonesian dictionary in both of LIWC and DAL, we have to automatically translate the transcription into English using machine translation.

because the psychological scores are calculated based on the word occurrences, incorrect word ordering in the translated text will not affect much.


There are also some possibilities that when the subjects answer the question with lying, the whole answer may show the deception cues.

taking consideration only some part of the whole answer can make us lose the pattern.


Language change through languages contact has been recorded in ways including lexical or structural borrowing [1].

structural borrowing, especially that of sound change, is less commonly documented.


They commented that such usage is "talking when biting the tongue", "pretentious" and "almost a kind of polluted language".

when the researcher asks younger participants (20s), irrespective of whether they do use /tʃ/ or not, they responded that "everybody does that", "it's cute and lovely" and "I think it is going to be a norm in the future".


Of course it was possible to update the vocabulary of the Automatic Speech Recognition (ASR) systems by adding new words to the language model.

as noted by (Logan et al., 2005), it could be difficult to obtain enough training data to train the language model for new words.


The matrix subject Salamtul 'people' cannot be the antecedent of caki in this sentence while nay 'I' can.

in the following example, as pointed out by an anonymous reviewer, the internal speaker Chelswu is much more likely to be the antecedent of caki here rather than the external speaker nay unlike (ii).


9 Zhangsan think Lisi to self not confidence 'Zhangsan thinks Lisi has no confidence in him/himself.'

(Pan 2001:280) Yoon (1989:486) points out that the incompatibility of caki with first or second person antecedents can be readily accounted for in terms of the notion of a logophor since it could be very awkward for an external speaker or an addressee participating in the current discourse to report their own thoughts or feelings in an indirect way.


6 In short, the blocking effect of the long-distance bound ziji has long been explained in terms of the notion of logophoricity.

it cannot elucidate the nature of the blocking effect properly.


In his analysis about the Korean causal connective -nikka, Park presents that if the proposition of the main clause includes an epistemic modal such as thullimepsta 'must' or -(u)l kesita 'will', -nikka encodes a reason to justify the outcome of the main clause.

if a modal is not realized in the proposition of the main clause, the nikka clause just expresses causality.


'was translated as 'he will be a policeman.'.

in this paper, it is translated as 'he will become a policeman' to make it clear that it has a predictive reading, as opposed to the epistemic will which was proposed by Huddleston as in "That will be the postman" which receives an epistemic reading due to the stativity of the complement verb 'be'.


Recently, Nevins and Myler (2014) have proposed an analysis of -ed adjectives of the type discussed here, where -ed adjectivizes categoryneutral √P, citing examples like *beautifulsinginged as an argument against the involvement of nominalizers.

their analysis has no way to account for the examples in (2) unless it is modified in such a way that -ed can also adjectivize nP, or alternatively, it is shown that the nominal bases in (2) are in fact √P, which is highly unlikely in face of the examples in (3).


This view receives initial support from the fact that some -ed adjectives (used to) have forms with and without be-: booted/bebooted, ringed/beringed, gartered/begartered, etc.

this null beprefixation analysis seems to be limited in its empirical coverage and work only for cases involving bare nouns, i.e., when the nominal base is simple in form, and it is not at all clear how this analysis could handle fair complexion and other modified nominal bases, as given in (2).


This amounts to saying that adjectives like crooked, ragged, and other adjectives with syllabic -èd are not denominal and on a par with allegèd and the like.

those with non-syllabic -ed undergoes the VI rule in (19)b.


21 The present analysis provides a simple and consistent answer to the cases we have seen so far.

leggèd and related forms appear to resist an explanation along the line suggested here.


22 Specifically, on the assumption that syllabic -èd appears as a result of Root-determined contextual allomorphy, leggèd is predicted not to appear in denominal adjectives owing to the VI rule in (19)b.

as (21) shows, leggèd can appear with a modifier unlike Root-derived adjectives.


ability to control Subject properties that are within the purview of the nominal in the lower Subject position.

it should be noted that we did not force an A-scrambling parse on the relevant sentences.


These series may affect the comprehension of the readers on the essay.

gonzales seemed to follow the principle of end weight in his distribution of series of adjuncts occurring in the same clause.


This series of temporal adverbials corroborate to the claim made by the authors that temporal adjuncts can be placed initially.

the position of the process adverbial in the series shifted the emphasis from the temporal adjuncts to process adjuncts.


(ii) The part of speech of the secondary connectives (their core words) should be on the first level, as it cannot differ in various connective or nonconnective usages.

the part of speech of a primary connective word can be differ-ent (at least for connective vs. non-connective usages), and therefore it has to be placed at the second level.


The above reports are confirmed again by Strik Lievers (2015) as well.

the universal tendency, as Strik Lievers (2015) noted, is not unidirectional but frequency-based.


This also includes seeing posts (10.7%), which also another way of checking information.

the other reported activities can be categorized as active activities because students have to do something to fulfil or accomplish certain tasks.


However, a generic and flexible method for semantic matching of short texts as in (Samuel, 2016) which leveraged word embeddings of different dimensionality obtained by different algorithms (GloVe and word2vec) and from different sources where the purpose was to go from word-level to short-text level semantics by combining insights from methods based on external sources of semantic knowledge with word embeddings.

(Prazak et al, 2012) attempt to estimate the similarity score between chunks based upon estimating semantic similarity of individual words and compiling them into one number for a given chunk pair.


It uses English and Japanese as pivot languages for Indonesian-Korean Translation (Balk et al., 2013).

it has been reported that direct MT model gives better performance compared to pivot MT model (Costajussa et al., 2013).


SRT format consists of a number indicating the subtitle's sequence, the start and end time the subtitle is appeared and the caption text.

sAMI file sets the time to milliseconds and the written style is similar to HTML and Css.


Although the nature of Bible words is different than the other corpus, this corpus may increase the BLEU score slightly because adding this corpus reduces out-ofvocabulary (OOV) problem, from 11.8% to 1.08 %.

because of the number of pairedsegments in Bible corpus is approximately 5 times than the other corpus, it contributes much more than the other corpus.


As we can see the result of the 1 st combination in Table 10, the untranslated phrases is no longer present in the translation.

there are more mistranslation phrase, such as "해" which is translated as "Syaka".


Learners of Japanese language from kanji background countries benefit substantially from kanji characters commonly used in both Japanese and Chinese when they read Japanese sentences or documents.

it is more challenging for them to read and learn various Japanese functional expressions with varied meanings and usages.


For example, JReadability 5 can analyze input text and estimate its readability to categorize it as belonging to one of six difficulty levels, on the basis of five characteristics: average length of sentence; percentage of kango (words of Chinese origin), percentage of wago (words of Japanese origin), percentage of verbs, and percentage of particles.

jReadability too does not sufficiently consider the various types of japanese functional expressions with varying difficulty levels.


Also, the concept of education in policy addresses in China often relies on the domain of BUILDING, with a focus on 'structure' and 'foundation' and a secondary focus on the concept of education as ENTERPRISE that can be 'invested' in.

hong Kong Policy Addresses conceptualize education as a PRODUCT that can be 'advertised' or whose 'quality' and 'quantity' can be 'improved'.


For exclusives, only and its counterparts are the most frequently mentioned particles supporting scalar use.

(See (3) for instance of scalar use of only) "only sentences" do not constantly express scalar meaning as the scale is derived from the context -both the existence of the scale and parameter of the dimension of the scale (See (4) as the example of non-scalar use of only) (König, 1991;Horn, 1996;Gast, 2012).


But many of the mophosyntactic analysis of Philippine languages remain unclear.

ergativeabsolutive analysis that came about in the 1980s with the works of Payne (1982) and Starosa (1986) and, Gerdts (1988) show viable results.


Since wh-in-situ languages like Korean allow wh-scrambling, the absence of wh-island constraints is accepted.

it is controversial whether whclauses can take a matrix scope or not.


As one of islands in English, whether-island constraints do not allow any phrases to be out of whether-clause, the sentence in (1) becomes ungrammatical.

wh-in-situ languages, such as Korean, do not exhibit such island effects as in (2).


Since the movement of the wh-phrase is semantically vacuous regardless of its surface position, Saito considered that this kind of scrambling can be undone or undergoes reconstruction into the indirect question at LF level.

miyagawa (2005) analyzed the same sentence as grammatical showing that it does not violate the wh-island effects in either at overt syntax or at LF.


Built on top of well-established lingustic theories, these studies focus more on "causal language" (expressions of causation) than real-world causation.

our ultimate goal is acquisition of real-world causal knowledge by exploting Wikipedia as an encyclopedia.


Many corpora for acquiring relational knowledge are created by annotating two entities and the relation between the pair of entities in a sentence (Doddington et al., 2004).

this approach is too difficult for crowd workers be-cause it requires locating the entities and considering the promotion/suppression relations for all possible pairs of entities.


When multiple overlapping spans are plausible, we need a criterion that prioritizes one span over the others.

such a criterion is difficult to define.


Of these two steps, feature selection is more critical since identifying the right features will guarantee any reasonable machine learning technique or classifier to perform well (Scott & Matwin, 1999).

feature selection is language-dependent.


Social media is an important source for news writers.

extracting useful information for news writers from the vast amount of social media information is laborious.


Negative samples were randomly sampled, so some positive samples are included in the data.

there are not that many positive samples 3 , so we regarded that their effect is limited.


On the other hand, their method presents tweets that have the highest possibility of being positive samples, making it possible to minimize the number of negative samples labeled by annotators and improving work efficiency.

when largescale incidents or accidents occur, secondary tweets such as retweets often occur, so the absolute number of tweets judged to be positive samples increases.


An analysis of some words associated with "victim" that can be found in table 2 in the Stanford Rape dataset reveal that many words that are associated with "victim" are still in reference to legal institutions -words such as "legal", "leniency", "crime" and "punishment" which are more tied to the legal aspect of the cases.

one set of word association results shows a break from this -when looking at associated words for "sentence" in table 3, there does appear to be a sudden association with the victim 6 months afterward with words such as "emily" and "victim" which were not present earlier.


A topic that would be close to the victim is the rape event itself which she herself narrated in the letter that she had written -a topic which can no longer be found in the later set's PCA results.

when PCA is applied to the overall dataset, the topic of the "victim" does emerge.


In general, a word segmentation problem can be modeled as a sequential labeling problem.

kyTea models such a problem as a binary classification problem that judges whether each two characters are segmented or not.


The input is the plain Japanese text file (sample.txt), and the output is that shown in Figure 1, i.e., the input texts are segmented into words, and the part of speech and the sense are assigned to each segmented word.

a sense is assigned for content words, including nouns and verb or adjective, stems, but for other kinds of words the sense s set to "0."


In general WSD, the sense list L w of the target word w is given in advance, requiring us to select only one sense in L w .


This problem has been ignored in conventional Japanese WSD.

it is a serious problem in Japanese all-words WSD, and we must take measures to address it.


In (93)-(97) are shown examples of the same tonal value for the main verb (i.e., Tone 1), but different tonal values of the direct objects from Tone 21 to Tone 4.

no tone sandhi occurs, which implies that these five combinations, (Tone 1 + Tone 21), (Tone 1 + Tone 1), (Tone 1 + Tone 2), (Tone 1 + Tone 3), and (Tone 1 + Tone 4), do not induce tone sandhi.


For the moment, only one clear tone sandhi rule is found in Twic East Dinka: Tone 4 Tone 3/ __ + Tone 4.

this rule might be exclusive to one specific structure (PST + N).


One disadvantage of chunk-wise translation is it cannot capture the con-text beyond each chunk's boundary.

despite this disadvantage, our Multi NMT model was still able to outperform a model that had access to a broader context (Multi NMT-sentence), indicating that our fixed-order translation approach is suitable for translating Japanese dialects despite its limited context sensitivity.


The Part-Of-Speech tag of a word can provide crucial information for a large number of tasks, and so, it is of utmost importance that the POS tagged data is accurate.

manually checking the data is a tedious and time consuming task.


This is one of the document classification tasks and supervised learning can be used to solve it.

if the domain of the test data is different from the domain for the learning data (for example, book reviews), the accuracy of the classifier obtained through standard supervised learning reduced.


From Table 3,it can be seen that SCL has no effect.After SCL is combined with our method, the accuracy is not high enough.

when SCL is combined with the proposed method, the precision for SCL alone is improved.


Hence a lot of the languages have only a small number of data points in our dataset.

we have ensured that the data collected, although small, is valid and collected from reputed movie review sites.


(HU4-1310) Example (4) demonstrates a copular construction predicated by the adjective incapable which is complemented by an of-phrase.

example (5) does not involve a copular construction but contains a postposed adjective suggestive which is also complemented by an ofphrase.


For example, important of is listed in all four subcorpora within the top 20 ranking.

upon a closer examination of the concordance lines showed that they did not carry a predicative role.


For instance, "extravagant luxury" as "奢侈的享受" and "extravagant habit" as "浪费的现象" are quite literally translated in view of the word order and the syntactic structure, especially as "奢侈" and "浪费" are often found in dictionaries as the context-free equivalents for "extravagant".

the first translation for each of the example source phrases obviously demonstrates a more contextually dependent rendition.


Some side support for this claim can be obtained from T1, which, typically in HKC, is a high-level tone with little changing pattern.

in LCY's speech, T1 was realized as its high-falling variant persistently, becoming more and more falling over time.


The study thus hypothesized that if the increase in pitch range and level was caused by hyper-articulation, the speech rate should have been slow down simultaneously.

in Section 3.2, the data illustrated that the speech rate of LCY increased from 1980s to 1990s and remained stable after 1990s, showing no correlation with pitch range and level and no evidence for hyper-articulation.


BLEU is the most well-known automatic evaluation technology in assessing the performance of machine translation systems.

bLEU does not know which parts of the NMT translation results are good or bad.


It has the strength of objectively assessing the results of machine translation.

bLEU is dependent on reference sentences and cannot point to translation errors in machine translation results.


This is supported by Figure 4 which shows the average confidence scores   In Table 5 the ROUGE-R scores for BOWLER-Top are consistently higher than that of other systems indicating that the sentences selected by BOWLER-Top are indeed more informative.

the ROUGE-P for BOWLER-Top, which is the least among all, affects its overall performance negatively.


L1 Burkinabe speakers generally become nativelevel speakers of French by adulthood, as French is the official second language of their country and is used in their daily life, but learn English as a foreign language is later in their lives.

as their L2 and L3 contain grammatical markings that are sometimes more similar to each other and sometimes more similar to the L1, such as with progressive/perfective constructions, it is unclear how this multilingual environment will influence the acquisition of such constructions.


earlier onset age) helps to develop learners' intuition about its grammatical rules, syntax, phonology, and more (Schouten, 2009;Newport et al., 2001;Johnson and Newport, 1989;etc.).

many researchers question it because there is a lack of consensus about the biological underpinnings of the theory, and much of the supporting research is contradicted by other studies or can be explained by a number of other factors (see Strid, 2017 for an in depth review).


Thus, there are multiple factors that could be thought to influence the acquisition of L2 French and L3 English by L1 Burkinabe language learners.

based on the previous research introduced here, some factors that could potentially have a strong influence include (i) onset age of language study, (ii) the number of years of language study, and (iii) transfer between L2 French and L3 English.


(Ayoun and Salaberry, 2008:560) French and English can both also mark the progressive aspect in the past tense by grammatical means, namely by adding were/was + -ing in English, or -aient, -ait in French.

it should be noted here that the simple present tense can also be used to express the present progressive aspect in French, as in (5a-b).


Thus, it follows that for L1 Burkinabe speakers, age of onset and number of years spent studying both L2 French and L3 English could have respective effects on both languages.

according to the studies concerning L3 acquisition, transfer from either the L1 or L2 is increased based on similarity to the L3, proficiency in the L2 and if the L2 was recently used.


their L1 or L2) has an aspectual system that is more transferrable to the L3 (English).

earlier learners of French may have over-relied on transfer from French, causing more errors because of the differences in French and English progressive aspect marking.


Our findings also suggest that transfer to the L3 is likely to occur from the L2 when the L2 and L3 are more similar overall, even if the particular morphosyntactic form in question is more similar to the L1 than the L2, which is only somewhat congruent with previous studies.

the fact that neither onset age nor years of studying the L3 were good predictors of L3 progressive marking indicates that transfer is the strongest influencer of L3 acquisition, which is congruent with the current body of L3 acquisition research.


And, among the subcategories of this verb category, ARGUE is classified into 'chitchat verbs' which is featured with not taking sentential complements (see 2.2a).

unlike chitchatted in (2.2a), ARGUE is compatible with this construction in another word sense of expressing a proposition (see 2.2b).


In terms of 'Issue', in general, they are mostly absent in the QUARRELING frame.

with a closer look, our results indicate their preference to co-occur with a group of 'Arguers' in a certain domain.


In other words, depending on the degree to which the metaphor is conventionalized in the language, it is expected to be far more difficult to detect using computational methods, since its domain essentially becomes indistinguishable from the rest of the context.

a metaphor such as "boiling-hot anger" is far more salient and would be expected to be easily detected by a context-based metaphor classification system, since the source domain (TEMPERATURE) is very distinguishable from the target domain (EMOTION).


Thus, we regarded this extended self as self-behavior.

"screaming" in the last sentence is only what the author did, which is as originally defined as selfbehavior.


We observed almost the same average percentage of promises in both the winning and the losing speeches.

other differences are important to observe.


When the entry is registered in the alignment table between the UniDic lexeme ID and the WLSP, we just choose one sense among the possible senses.

in the case of LUWs, most of the tokens are not registered in the alignment table.


This issue is inevitable as long as the principle of bivalence is adopted, because there are cases where truth value of sentence prefix cannot be determined.

this is not the case for our proposed incremental interpretation, because it allows sentence prefixes to have truth-value gaps, i.e., to be neither true nor false.


there is no surprise for a relation between "mackerels" and "blue."


This is one reason that the feature generated the good performance in Table 5.

it should be in ascending order from the assumption described in Section 4.2.1.


In the experiment about the trivia score estimation by using SVR, we did not always obtain the success as compared with the naive baseline.

in the experiment about the ranking task, the SVR obtained 0.791 on nDCG@5 and the RankNet-based method obtained 0.782 on nDCG@10.


These variations have been found in a range of linguistic categories, such as phonological, syntactic and even discourse.

many of the current studies take Mainland China Mandarin (MCM) as a yardstick and investigate only the features that deviate from MCM .


With two different possible sources for one construction, many questions can be raised, such as if there are any differences between the two given their potentially varied origins.

as most studies compare these non-MCM varieties against MCM, these questions remain unanswered.


To achieve high accuracy, modern Japanese word segmentation (JWS) methods utilize discriminative models relying on extensive feature engineering.

machine-learning-based methods tend to require hand-crafted feature templates.


The dimension of character embeddings is fixed by following (Chen et al., 2015b).

we search six configurations of charactertype embeddings: 1, 3, 5, 10, 20, and 50.


There are several attempts to incorporate neural representations into a conditional random field (CRF) (Ma and Hovy, 2016;Lample et al., 2016;Peters et al., 2017), all of which use bidirectional LSTM as encoders for sequence labeling tasks.

we apply simple n-gram embeddings, which can be easily obtained using a raw corpus.


In the first example, "と (and)" and "りゅう (dragon)" are different words.

they are of the same character type "Hiragana."


Another popular approach employs pointwise prediction by using a local window (Neubig et al., 2011;Neubig and Mori, 2010).

both approaches do not consider the global context because they use feature templates of a fixed length.


The Encoder-Decoder model is widely used in natural language generation tasks.

the model sometimes suffers from repeated redundant generation, misses important phrases, and includes irrelevant entities.


These models take advantage of the fact that machine translation is the loss-less generation (lossless-gen) task, where the semantic information of source-and target-side sequence is equivalent.

as discussed in previous studies, abstractive summarization is a lossy-compression generation (lossy-gen) task.


Therefore, we compute the frequency of tokens that occur more than once in the generated headlines.

some phrases might occur more than once in the gold data.


The first sentence in the reference mentions the result of a game.

it is difficult for all models to generate this information because it is not described in the article.


This is because the second and third sentences in a parallel type summary are in no particular order.

the score of the sentences in the reverse order is low (e.g., the scores shown in bold in the row of '213' and the second column).


In this work, we use alignment information to alter input sequences because in low-resource settings, representation is crucial for neural networks models.

the source and target embeddings will be pre-trained with the training data.


There is no way to use a Cantonese EI without expressing speakers' ignorance.

the reportative implication in RIs is encoded by wh-doublets.


In EIs, m-zi and the following wh-expression are always adjacent.

aaming M-ZI eat-PERF what SP the adjacency requirement for RIs is obvious.


Although the embedded questions in (47a) and (47b) presuppose Aaming ate something, it can be retreated by (48).

the infelicity of (48) as a continuation of (47c) suggests that the presupposition cannot be retreated.


Yuan and Briscoe (2016) presented the very first NMT model for grammatical error correction of English.

word-based NMT models usually suffer from rare word problem and infrequent words are substituted for a "UNK" token.


First, the model trained on only edit logs (UDN-only) performs well on UDN Edit Logs but very poorly on SIGHAN-7.

the model trained on only artificially generated data (ART-only) has worst performance on UDN Edit Logs but acceptable performance on SIGHAN-7 Second, it is worth noting that the model trained with sound and shape features has significantly better accuracy, recall, and F1 score on UDN Edit Logs.


This seems to suggest that T3S items and T2 control should be similar in terms of tonal characteristics.

figure 3 demonstrates that it is not the full story.


Regarding rime types, our results of mean F0 and F0 difference supported Xu's (1998) argument that the tonal alignment "is not affected by the internal structure of the syllable in any direct way, further confirming the hypothesis that the syllable is the only relevant unit for the alignment of lexical tones in Mandarin"(p.200).

in terms of turning point, VG had a significantly later turning point than V, and VN had significantly earlier turning point than V. Further research can more clearly reveal the relationship between the turning point and the intrasyllabic structure.


We can also see that the models would be impaired, no matter which component is removed.

the accuracy drops slightly than the first group (1.6% absolute vs 0.5% absolute, and 1.3% absolute vs 0.7% absolute).


Generally, languages acquired after the first language (L1) are usually called second languages (L2).

consensus has not yet been reached on the definition of the term 'the third language (L3)' (De Angelis, 2007).


Tukey comparisons showed significant differences between similar and new vowels (/oe/-/ɔ/: p<.001, /oe/-/ɛ/: p <.001, /y/-/ɔ/: p = .042, /y/-/ɛ/: p<.001, Bonferroni-adjusted), indicating that students got much less accent when producing similar vowels (mean for /ɔ/ = 5.49, mean for /ɛ/ = 5.71) than new vowels (mean for /y/ = 4.68, mean for /oe/ = 4.6).

no distinctions were found within similar (/ɔ/-/ɛ/: p = 2.29, Bonferroni-adjusted) or new vowels (/oe/-/y/: p = 4.74, Bonferroniadjusted).


As shown in Table 1, the amount of Cantonese use and Urdu use was prominently correlated with accent ratings in all models, with the Pearson coefficients varying from .689 to .784 for Cantonese use, and from -.532 to -.325 for Urdu use.

english use exerted no significant effect in both the simple (χ 2 (159) = -.062, p = .434) and the 7-removed (χ 2 (152) = -.092, p = .254) models, indicating that the english use was the least influential variable among the three language use factors.


Vowel type showed consistent influence (χ 2 = .205 ~ .39) in the results.

aOL was hardly (χ 2 = -.158 ~ -.033) linked to learners' accent scores in most cases and English use was also detected to be an insignificant (-.164 ~ -.062) contributor to most models.


Children who start learning a non-native language within the critical period might have a good chance of approximating a more native-like production than late language learners (DeKeyser, 2000).

flege and Liu (2001) did not totally agree with this view because there are still adult learners who learn a new language late but gain a native-like articulation without a detectable accent.


This proves that the more training data are used, the better W is.

good intermediate results do not necessarily generate good final results.


On one hand, this may facilitate the differentiation between Sino-Korean words and non-Sino-Korean words by Chinese learners of Korean as an L2.

this results in a large number of homophones in Sino-Korean words which may cause confusions in reading.


these datasets cannot be applied to our task because they do not contain vocabulary restriction.


Moreover, "EncDecAttn" performs a large number of changes with very few mistakes.Hence, "EncDe-cAttn" is the best model with regard to the NOR-MAL dataset.

in the RARE dataset, the fluency and adequacy scores of the "EncDecAttn" are low, i.e.


(2015) proposed a model called distillation and were able to train a model which was more compact than the original one.

it needs a lot of time for training since it needs to be trained again for distillation.


The softmax yields a label distribution y ∈ R d l , following which the classifier chooses the best label corresponding to the highest element among the y.

owing to the lack of phrase-level annotation, sentence representation may be inaccurate because it may fail to propagate errors from the root of the tree to the terminals and pre-terminals in a long sentence.


2010is that we use polar dictionaries as a hard label in a manner similar to distant supervision (Mintz et al., 2009).

the previous work used polar dictionaries as a soft label for an initial hidden variable in Tree-CRF.


This validates that our 2-stage pipeline using the information of the web genre significantly improves the performance of content extraction systems.

there is still a gap between GOCE and oracle mode due to incorrect web genre classifications, which decreases F 1 score by 0.23%.


However, the scope of these methods is limited to individual sentences and ignores the relationships among sentences.

some methods are aimed at identifying the translation by the POS N -gram model (Arase and Zhou, 2013;Aharoni et al., 2014), but they extract features from only a few adjacent words.


Other methods identify the translated documents  or generated papers (Labbé and Labbé, 2013) using word distribution.

such methods are only suitable for huge text.


The examples of successful selection were found in the case of " ", " (/kanki/ (cold air) or /samuke/ (chill))" and " (/han getsu/ (half moon) or /han tsuki/ (half a month))."

a failed example was found in a short word such as " /sora/ (sky)," " (/jin/ or /nin/)," and " (/toko/ or /yuka/)."


For example, (/kara/ (empties) or /sora/ (sky)) can be replaced by " " /karaqpo/ (empties) or " " /o sora/ (sky, a courteous word for sky), (/toko/ (bed/alcove) or /yuka/ (floor)) to " " /ne doko/ (bed) or " " /furoaa/ (floor).

the longer word sometimes matches beyond the correct word boundaries.


Estimating the intensity of emotion has gained significance as modern textual inputs in potential applications like social media, eretail markets, psychology, advertisements etc., carry a lot of emotions, feelings, expressions along with its meaning.

the approaches of traditional sentiment analysis primarily focuses on classifying the sentiment in general (positive or negative) or at an aspect level(very positive, low negative, etc.)


For the classification subtasks EI-oc and V-oc, we use the additional metric Pearson correlation calculated only for some emotion like low emotion, moderate emotion, or high emotion.

for the multi label emotion classification E-c, we used the official evaluation metrics Jaccard Similarity (accuracy), micro average F1 score and macro average F1 score of all the classes.


Later, (Qi et al., 2009) proposed an effective neural network model using the Convolutional neural network (CNN) combined with CRF.

this was purely based on word embedding and not capable of processing variable length input.


(Hsu et al., 2008) and (Murugesan et al., 2017) have successfully experimented BI-CRF in the field of medicine for NER.

these approaches purely rely on BI-CRF, thus fail to utilize neural networks to automatically learn character and word level features.


Yet another direction of research would be to base the filtering process on supervised learning, so we can set the number of patterns retained to optimize the accuracy rate.

the resulting patterns and collocations extracted from the large-scale corpus crucially reveals many interesting phenomena for linguistic researchers.


Semantic Role Labeling (SRL) has been extensively studied, mostly for understanding English formal language.

only a few reports exist for informal conversational text, especially for language being used in the chatbot system.


They trained the PAS analyzer for dialogues by using newspaper articles.

pairs of other media types have not been investigated yet.


However, pairs of other media types have not been investigated yet.

we target various types of Japanese texts; we use Balanced Corpus of Contemporary Written Japanese (BCCWJ) 1 (Maekawa et al., 2014) for evaluation.


(2014) proposed a CNN network integrating with position embeddings to make up for the shortcomings of CNN missing contextual information.

the features extracted by simple RNN and CNN are still not sensitive to location information which may miss core semantics.


Supervised learning has achieved great success In many natural language processing tasks.

in real applications, the source domain S in the learning stage may be different from the target domain T in the testing stage, In this case, the classifier learned in the source domain S is ineffective in the target domain T .


On the contrary, DBoW is an application of Skipgram in word2vec.

to CBoW, Skip-gram predicts the words around the target word, and its input is a word.


In contrast to CBoW, Skip-gram predicts the words around the target word, and its input is a word.

in the case of DBoW the input is not a word but a document ID.


Normally, they also strongly expect that doctors will diagnose their illnesses and treat them correctly.

because of the lack of medical knowledge, severity of illnesses as assessed by patients might be incompatible with doctors' assessment.


This was also regardless of the item types.

if the information given by doctors strongly contradicted participants' expectation (i.e., perceived severity) and affected their comprehension, participants might ignore the hedges used.


For the perceived-to-beunserious type, the medians of both sentences with low-possibility hedges and sentences with highpossibility hedges went up to 5.

the Bayesian cumulative link mixedeffects model indicated that none of the main effects nor the interaction was observed (possitility, β̂ = -0.18, 95% CrI = [-0.51, 0.14]; perceived severity, β̂ = -4.62, 95% CrI = [-15.06, 7.24]; degree of perceived-severity, β̂ = 1.49, 95% CrI = -4.16, 6.56]; interaction between certainty and perceived severity, β̂ = -0.23, 95% CrI = [-0.89, 0.42]).


According to the Bayesian cumulative link mixed-effects model, there was neither main effect of possibility (β̂ = 0.18, 95% CrI = [-0.12, 0.46]), main effect of perceived severity (β̂ = 3.31, 95% CrI = [-4.60, 10.39]), nor main effect of the degree of perceived severity (β̂ = -1.22, 95% CrI = [-4.87, 2.55]).

there was an interaction between possibility and perceived severity (β̂ = -0.85, 95% CrI = [-1.49, -0.23]).


The study on the processing of possibility hedges in unexpected text continuations showed that the difference in the possibility hedges used did not affect the degree of participants' surprise.

for the text credibility, it was found that participants paid more attention to possibility hedges when text continuations turned out to be worse than expected than when the continuations turned out to be better than expected.


To further enhance the performance of depression detection, researches have recently proposed deep learning methods (Kshirsagar et al., 2017;Yates et al., 2017;Orabi et al., 2018).

despite the improved performance, they still do not present a clear way to explain why certain individuals have been detected as depressed, which is critical for a follow-up study on specific diagnosis and prevention.


(2018) interpreted lexical-semantic meanings and grammatical roles for each word by interpreting learned internal representation, which is in contrast to looking at the input patterns to interpret activated internal neurons.

interpreting a result by analyzing attention or neurons has a limitation that it cannot provide a rich explanation.


For example, the word 'anxiety', which has negative polarity, does not appear in the group of low F2 weighted posts from users in TP-High and TP-Low classes.

in the group of the high F2 weighted posts, it appears 2,233 times in 29,360 posts, which means that 7.6% of the high F2  weighted posts contain this word.


Compared to other feature networks, F4 (writing style) has little effect on detecting depression.

we found that when the F4 weight increases, the number of verb phrases is also increased.


Evaluation studies typically rely on automated evaluation metrics as they are cheap to compute and scale well.

these metrics are not designed to assess automated highlighting.


However, the reason for low performances on LOCATION tags seems not the low performance of the POS tagging because the microand macro-averaged F-measure for both corresponding tags were approximately 90%.

when comparing the place names tagged with these tags in the corpus, there was a difference between two tasks.


Like the post of example (1), this post also contains a key phrase "misinformation."

this post is not an SCP one (−1) because it just expresses the user's desire by the phrase "I wished it had been misinformation."


4 I continue to gloss -se as CAUS or C. 5 As we will see in section 4, I argue that the agent argument in (11) is originally introduced as an underspecified argument and later interpreted as such at the C-I interface.

a more specified, (proto-)agent argument is suppressed in niyotte-passives.


In this connection, consider 7 If both qu and k'ɯ perform the assertive function, the prediction is that the deletion of them should be equally felicitous, because in this case, a final particle only serves to reinforce the assertion.

the prediction is not completely borne out.


If all the sentence final k'ɯs perform the assertive function, the prediction is that the deletion of k'ɯ should be equally felicitous, because in this case, the final particle k'ɯ only serves to reinforce the assertion.


By treating modal semantics as ambiguous, the monosemous notion provides a common ground for the interpretation of different senses expressed by the modal verbs.

boogaart points out that such an abstract notion tends to stretch the unitary semantics so much that it fails to capture modal meanings in real use.


The primary goal of human language is to communicate.

how communicative efficiency of a human language can be quantified is not a simple question.


There has been a lot of work done on each of these tasks separately, where the features and techniques chosen for classification are specific to the particular task (Silva et al., 2011;Huang et al., 2008;Pang and Lee, 2005a;Kouloumpis et al., 2011).

the recent popularity and developments in Neural Networks have enabled researchers to build classifier models that are more generic in nature (Kim, 2014;Kalchbrenner et al., 2014).


These words are the same as in all other dictionaries.

most ambiguous words are not assigned a default polarity because ambiguous words (the second group) may have sentiments that depend on contextual words.


This study's primary purpose was to predict the sentiment polarity of each review as positive, negative, or neutral.

the sentiments of neutral reviews were very difficult to predict because the customers used only one kind of sentiment word.


The consensus is that haplology has to meet two requirements: (i) juxtaposed lexemes must have the same sound; (ii) juxtaposed lexemes must have the same meaning.

in reality haplology does not always happen when the requirements are met.


The more frequent a sequence is, the more effort is saved by eliminating a duplicated unit.

when frequency is low, the principle of economy does not have much to gain.


We confirm the facilitating effects of frequency and the blocking effect of tone sandhi.

we found no clear effect for disyllabicity contrary to claims in earlier literature.


Hence, yídìng in this example does not express strong epistemic necessity, but an emphatic reading, which is referred to as an intensification reading in Wu (2018b).

in (4b), cāi 'guess' takes a stipulation or conjecture as its complement.


mode of truth)", and that epistemic modality refers to "the state of a proposition in terms of knowledge of belief (i.e., modes of knowing)."

although it is possible to provide distinct definitions for alethic and epistemic modality, the line is not that easy to draw.


As we can see here, Palmer suggests that there is no cases of alethic modality in natural language.

discussing subjective and objective epistemic modality, Portner (2009:122-123)  There is no doubt that both (8a) and (8b) are uttered, based on the speaker's knowledge, although in (8b) this source of information is explicitly stated.


6 While the speaker's knowledge is required to utter both (8a) and (8b), this requirement does not make either (8a) or (8b) a modal statement.

both (8a) and (8b) are bare declarative sentences.


All intransitive verbs are either unaccusative or unergative, which can be identified through language-specific unaccusative diagnostics.

different unaccusative mismatches, as mentioned above, indicate that it is quite difficult to fit many verbs unambiguously into one class or the other.


This leads Sorace (2004) to the conclusion change of location (arrive) core unaccusative change of state (decay) continuation of state (stay) existence of state (exist) uncontrolled process (sweat) controlled motional process (swim) controlled nonmotional process (work) core unergative that it is the position of verbs on the SIH that decides the order of acquisition of unaccusative syntax.

it still remains unknown whether English, a language without auxiliary selection, also exhibits the same gradience in split intransitivity.


Non-alternating verbs of change of state like decay and appear are also allowed in the construction such as in the decayed building and a recently appeared novel.

some monadic verbs of change of state such as die and happen are incompatible with this construction as illustrated in (4).


Because each <HEAD, DEP> acts as a single unit in linguistic analysis, the task is to predict a collocation include head word and dependency word given the other words in a context as follows: So despite the fact that the <HEAD, DEP> collocations vectors are initialized randomly, <HEAD, DEP> can eventually capture syntactic and semantic features as a production of the target probability task.

by mapping each collocation to distinct vector representation, the performance must get worse as millions of VP collocation data are available.


In this case, the more similar the text style, the more effective LM embeddings will be.

we can't get unlabeled dataset which is highly correlated with the labeled dataset.


In recent years, researchers mainly have been using CRF (Lafferty et al., 2001;Sutton and McCallum, 2012;Wang et al., 2015Wang et al., , 2016a to recognize named entity.

cRF needs to manually set features and requires a large number of professional domain knowledge (such as digital dictionary of location names), manually, it has poor generalization ability in new areas.


Concatenate the forward and backward LM embeddings to form bidirectional LM embeddings, i.e.,  The embedding learned from BLSTM and LM, have various degrees of contribution to tag prediction.

concatenation fails to consider the imbalance between them; therefore, we aim to improve by using the attention mechanism (Rei et al., 2016).


The nominative argument of "open" is "cafeteria" which appears in the same sentence (intra-sentential zero anaphora) and the dative argument is "the nearby shopping district" which appears in the previous sentence (intersentential zero anaphora).

the nominative argument of v 1 (see) is the writer of this text who is not explicitly mentioned in the text (exophora).


(2013) proposed a zero anaphora resolution method for both intra-and intersentential anaphora.

they evaluated their method by using only Web text corpora.


This algorithm searches for the combination of a case frame candidate and argument candidates for the given predicate that minimises the distances between the vectors.

since KUCF was constructed by an automatic method, there can be wrong classification of case frames, i.e.


Effect of word embedding and MVC Introducing word embeddings of the arguments and the predicate (F1) into the baseline model (F0) degrades the total accuracy.

replacing the predicate embedding with the MVC (F2) increases the accuracy in comparison with F0.


No matter in segmental features or suprasegmental features, the effectiveness of the training has been observed via vast experiments.

perrachione (2011) indicates that HVTp enhanced learning only for individuals with strong pretraining aptitude, and for the individuals with low pretraining aptitude, the HVTp may weaken their identification ability.


Although they share the writing system, orthography is not considered to be a proper module of a linguistic system and was not deemed relevant as evidence for language status in most linguistic theories.

chinese orthography is a writing system consisting of both the signifiers and the signified.


To the best of our knowledge, only two processing studies have invited native speakers to rate the semantic transparency of radicals (Feldman and Siok, 1999;Law and Yeung, 2010).

the number of native speakers in these studies was not sufficient to generalise the results to the whole native population (only ten were involved in the former and twenty in the latter).


Due to the inanimate nature of trees, it is difficult to derive a concept in the 'participant' relation, as 木 mu 'tree' would hardly function as an agent or experiencer in an activity.

for the category 'agentive', it is difficult to explain the reason behind the observation.


The mutual intelligibility test is motivated as a simple test of systembased similarity and a language's main communicative function.

it has been applied exclusively at the speech level thus far.


This is puzzling on one hand, but it is also widely accepted by linguists when terms such as Old English and Middle English are given.

recall that time is continuous; thus, we have to assume that the English we speak today is the English we spoke yesterday, and that the English we speak in the next ten years will be the English we spoke in the past ten years.


Another is phonological facilitation effect, which shorter response time and higher accuracy to name the target picture when the distractor word and the target word share some phonological feature (e.g., onset) than when the distractor doesn't share any phonological feature with the target word.

see from a review by Abdel Rahman and Melinger (2009), whether semantic interference effect will happen are highly reliant on control of the degree of semantic related.


It can speed response time whereas it may also cause large confusion when participants respond without complete the whole lexical production process.

for low proficiency participants, the accuracy in the auditory experiment is not significantly different with the accuracy in the visual experiment.


The accuracy of phonologically related distractors of the auditory experiment is significantly higher than that in the visual experiments.

the accuracy of phonologically related distractors of the auditory experiment is significantly lower than that in the visual experiments.It seems that auditory phonological cues have a limited facilitation effect compared with orthographic cues for the late Chinese-English bilinguals in this study.


The cohort of late bilinguals may not be proficient enough to identify orthographic cues of phonologically related distractors which only a few initial letters are the same as the target word.

they can figure it out fast when the distractor words are congruent with the target words that complete orthographic cues were presented.


In auditory modality, the more phonological cues of the target word involved in the auditory distractors, the stronger facilitation effect will happen on latencies and accuracy.

from the comparison between visual modality and auditory modality of phonologically related distractors and congruent distractors on accuracy, it seems that when both phonological and orthographic complete cues are provided, phonological cues may have a limited facilitation effect compared with orthographic cues for all the late Chinese-English bilinguals in this study.


The overall results demonstrated that it did not, which may appear to challenge the analysis of Ascrambling as movement to SpTP, the high subject position.

if we adopt the distinction between low and high subject positions and assume that HA and PC are low subject properties, the apparent challenge can be dismissed.


In this case, age-related variation can index sociolinguistic variation.

age-variation also contains possible anatomical variation (Chatterjee et al., 2011).


Such kind of data is often analyzed to identify theft, fraud or scams for the purpose of solving criminal case.

the most common way to collect lies employed by the researchers is to collect them through controlled experiments.


Therefore, the language they use in such lies might not be the same as lies in real life situation.

the advantage of this method is also very clear, that is, researchers have more control over the number and the kind of data they want to collect.


Also, in deceptive descriptions, there are more negative emotions and motion words (Newman et al., 2003;DePaulo et al., 2003).

the results of some research are inconsistent with the findings mentioned above (Hauch, Blando ń-Gitlin, Masip & Sporer, 2012).


Thus, they exert an important role in the field of cybersecurity.

state-of-the-art IOCs detection systems rely heavily on hand-crafted features with expert knowledge of cybersecurity, and require a large amount of supervised training corpora to train an IOC classifier.


Therefore, the model without spelling features fails to identify those low frequency IOCs for the lack of context information, while the proposed model correctly identifies those IOCs using spelling features as extra information.

model with hand-crafted spelling features may cause more extraction of false positives, i.e., tokens that have similar to IOCs but are not malicious.


extractive summaries of multiple web pages retrieved using the place names which bias the summarizer to include sentences mentioning these place names tend to be better than generic summaries of the same pages.

the resulting summaries were still far from ideal.


This would be the case for all search engine queries which do not contain a specific place name but rather are more general query such as parks in Barcelona.

one could argue that the same benefits would be achieved by simply taking Wikipedia articles as image captions, rendering multi-document summarization unnecessary for captioning.


when engineering features robust for domain shifts.

a shallow approach also has its shortcomings, an important one being that prepositional phrases, which contain important semantic information for interpreting events, are left unattached.


All the abovementioned features make blogs a valuable source of information that can be exploited for different purposes.

due to their language being heterogeneous, it is complex to understand and formalize in order to create effective Natural Language Processing (NLP) tools.


They are then classified to determine the polarity of the question, using the WordNet Affect emotion lists, the emotion triggers resource [17], a list of four attitudes containing the verbs, nouns, adjectives and adverbs for the categories of criticism, support, admiration and rejection and a list of positive and negative opinion words taken from the system in [18].

we preprocessed the blog texts in order to prepare the answer retrieval.


In many cases, opinion QA can benefit from a snippet retrieval at a paragraph level, since usually the answers were not mere parts of sentences, but consisted in two or more consecutive sentences.

however, we have seen cases in which each of three different consecutive sentences was a separate answer to a question.


As one can see from Table 4, the recall for the superordinate relation is very good and the precision of the patterns is not bad either (average precision 85%).

many of the extracted superordinate properties are roles and not types.


Subordinated sentences are formed in Basque by attaching the corresponding morphemes to verbs, either the main verb (non-finite verbs) or the auxiliary verb (finite verbs).

in BDT the verbal elements are organized around the main verb (semantic head) while the syntactic head corresponds to the subordination morpheme, which appears usually attached to the auxiliary.


The content selection procedures outlined previously concentrate entirely on relevancy, not redundancy.

in multi-document summarization, presented content should be relevant to the query and novel with respect to what is already mentioned in the summary.


The previous section outlined a comparison of different configurations of the summarization framework.

the way the graph weight configurations are determined implies that the weights are tailored to the DUC 2006 data set.


We obtained very competitive results during TAC 2008 on the "Opinion Task".

our system did not rank as well on the "Update Task".


One can see that CBSEAS 0.5, just by using this trick, obtained significantly better results than CBSEAS 0.2.

cBSEAS 0.5 is still not among the ten best systems.


Our system does not use such information.

the studies on which those features are based neither take into account the document type nor its structure.


It can be compared to approaches using sentence neighbourhood [4], because the sentences which are highly related to the highest number of sentences are those which will be extracted first.

our approach is different since sentence selection is directly dependent on redundancy analysis.


Word generalization has more impact on the computation time for shorter N-grams, and in particular for single words (see Section 4.2.1).

we do also notice some improvement for 4-grams.


A similar approach, described in (Adolphs, 2008), applies nite state techniques to generate possible inectional classes for unknown German words.

disambiguation is done by using metrics based on frequency counts obtained from a corpus.


In (4-a) r is doubled to keep the vowel short.

this is not the case in (4-b) because the stress falls on another syllable and thus, e turns into a long vowel.


Superlative is formed by adding the sux -st to the base form: snel -snelst.

base forms that end in -s take only -t to form superlative: geeps -geepst (palethe most pale) Additionally, when adjectives are used attributively, they get an -e sux.


It is formed by adding the prex ge-to the stem and, depending on the nal consonant of the stem, either a t or d sux is attached.

if the stem already ends in -t or -d, no sux is added.


This is due to the fact that those words have at least one irregular form in their paradigmsschip-schepen (ship-ships).

we do not see these irregularities as an obstacle for the performance of our method since they form a closed class and are supposed to be already included in most lexicons and dictionaries.


To prevent this, we search for the combination of the indenite article een and the root.


There are 188 cases of morphological ambiguity.


After the disambiguation phase, only the verb and the adjective paradigms for gebruind survived' and they are correctly taken to be the nal outcome.

in very rare cases there is morphological ambiguity within the same POS.


The alternative open-source WordNet for French called Wolf (WordNet Libre du Francais, [24]) remedies the first shortcoming (restrictive licensing) and aims to achieve a wider coverage by automating the WordNet construction process using an extend approach which in essence, translates the synsets from Princeton Word-Net (PWN) into French.

compared to Morphalou, Wolf is still incomplete (979 verbs and 34 827 nouns).


To some extent, these lexical networks enable segmenters to rely on a sort of concept reiteration.

their lack of any topical structure makes this kind of knowledge difficult to use when lexical ambiguity is high.


Although this evaluation tells us that textual presentation can be very effective, it does not give direct information about which aspects of the experimental texts played a role in decision-making.

the significant effect of main target action does suggest that some of the burden is carried by the content selection strategies in the H and C texts, with the human-authored summaries incorporating more information that was relevant to the appropriate actions.


No significant correlations (r) were observed between NE or NW and performance in the H condition; in the C condition, both correlations are significant.

in all cases, the r scores are negative, suggesting that more information tended to be linked to lower decision-making performance.


As for the correlations within each condition (r), they are once again negative, and never reach significance.

we do observe a significant positive correlation between difference in performance and both REL and IRREL.


However, it is often useful to create features based on the conjunction of several simple predicates: The above feature could be useful for disambiguating the token Batenberg 1 ('Batenberg'), which can be a person's name (e.g., when followed by upravl va, 'ruled ').

it could be also part of a location (e.g., when preceded by plowad, 'a square'), and thus we might want to have a special feature for this case: The system already uses tens of thousands of features, which makes it infeasible to create predicates for the conjunction of all pairs of simple predicates.


Our experiments show that crf models with carefullydesigned features can identify mentions of named entities (organizations, persons, locations and miscellaneous) in Bulgarian text with fairly high accuracy, even without features containing domain-specific knowledge.

such features, which in our framework take the form of membership in a gazetteer, simple common endings for personal names and location entities, and rich morpho-syntactic tagsets, can lead to improved system performance.


This evaluation is similar to the one relying on manually assigned user-features.

instead of using the user annotations, we use the output automatically predicted by the classifiers.


[Shen & Joshi, 2005] use the term 'semi-incremental' to refer to parsers (both left-corner [Collins & Roark, 2004] and head-corner [Yamada & Matsumoto, 2003]) which permit multiple iterations of left-to-right scans, rather than just one.

to these models, [Shen & Joshi, 2005] introduce an approach for incremental parsing of spinal Lexicalized Tree Adjoining Grammar, which supports full adjunction, a dynamic treatment of coordination, as well as non-projective dependencies.


disambiguation is a prerequisite for any effort at precise error detection.

a grammar error may disturb the disambiguation, .


Thus CSLR-rep reduces processing time linearly.

the number of combination of features exponentially increases.


event argument: position: chief; person: "Barry Diller" As for other ACE tasks, the ACE 2005 official evaluation scorer can produce an overall score called "ACE value" for event extraction.

most of the ACE event extraction literature (e.g.


The results are reasonably useful for hundreds of documents.

when we apply the same system to process much larger corpora, the net result is a very large collection of events which are: (1) Unconnected.


Driven by the requirements of the particular application (speechto-speech translation), the focus was not only on parse selection, but also on combining incomplete results.

no quantitative evaluation of the results has been published.


the effect of negation ('I don't like ..').

sentiment composition is a phenomenon that can be fixed with a relatively small set of simple rules with very few exceptions.


Thus, word sense disambiguation is a necessary component for many applications in Computational Linguistics, such as machine translation, information retrieval, information extraction, or question answering.

most of the WSD approaches reported so far rely on supervised learning techniques, relying on the data sets provided by the SensEval 1 and SemEval 2 competitions.


Filtering the additional examples to maintain the original distribution of senses improves results, but they still do not reach the quality of supervised training only.

filtering the additional examples for quality does increase overall performance slightly.


In fact, lately, several systems have been developed for shallow semantic parsing and semantic role labeling using these resources [11], [26], [14].

building large and rich enough predicate models for broad-coverage semantic processing takes a great deal of expensive manual effort involving large research groups during long periods of development.


It seems that for verbs it is useful to consider not only the disambiguated words but also the most frequent senses of the rest of words being disambiguated.

for nouns and adjectives the best precision is achieved by the original SSI-Dijkstra.


Doing so naturally increases all word frequencies, turning low frequency words into high frequency ones.

new words are always introduced meanwhile, that bear low frequencies.


The only way to make a period become a hapax is to cut the corpus down to one sentence only.

all words on this sentence would become hapaxes as well, which prevents them from being aligned separately.


Hence, the number of random subcorpora of size k to forge by sampling is defined by (1 − k/n) kx ≤ t, which yields: x ≥ log t k log (1 − k/n) Processing at least x random subcorpora of size k will thus ensure a proper coverage of the input corpus.

rather than setting in advance some particular degree of coverage (hence imposing a fixed number of subcorpora to process), we deduce from the above result a probability distribution to randomly draw the sizes of the subcorpora to process: The numerator (log t) was substituted for −1 because t is a constant: t ≤ 1 ⇒ log t ≤ 0.


The results for TIMEX3 recognition using 5-fold cross validation were 89.6% and 81.7% F β=1 for relaxed and strict span.

ttK [22] accomplishes this task using the GUtime module.


Results show that, taking TSR as TIMEX3 without any post processing (Baseline), they are reasonably good in the span relaxed identification case, but not in the strict case.

tIPSem approach obtains much higher results.


Corpora, which are defined as large bodies of linguistic evidence composed of attested language use [9], are increasingly used in natural language processing field(nlp), such as in machine translation, automatic summarisation, etc.

the diversity of nlp tasks that are based on corpora is proportional to the variousness of types of the latter.


The extracted linguistic features will be used by our system for the identification of candidate keywords, in the form of a morphological filter which only permits phrases having certain morphological structures to be considered by the user (noun, adjective, verb, etc.).

in the current study the morphological category was not restricted and all types of candidates were considered.


These metrics were chosen as starting point for this preliminary study due to their extensive use and simplicity.

more complex formulae will be used in the future, which might generate better results.


A match is counted if a term from the list returned by the system is present in the golden standard in the exact variation form.

problems may occur in the case of orthographic variations (oxidization vs. oxidisation) or syntactic variations (lung cancer vs. cancer of the lung).


From another side, the speech in the studied bimodal corpora was manually transcribed in order to avoid the negative effects of automatic speech recognition systems on this preliminary study (mainly due to the noise and error rate).

in the future, automatically transcribed speech should be considered.


We are convinced that this approach is closer to real-world situations since we cannot be sure that the training data will be from the same time period and thus cover the same topics.

we make sure that all instances of a word are put in the same fold.


sentiment roles or product features) are usually included per text region.

we wish to evaluate all entity markers in a given text region.


It is apparent that the task is highly subjective because the figures are only modest in a three-way condition (accuracy 62%; k .43∼.45) (see §4.4).

the task is considerably less vague in a two-way non-neutral condition (86∼89% accuracy; k .70∼.78).


Reusing available linguistic resources is a handy way to start developing new ones.

it requires to interpret all input resources even though their lexical models are partially incompatible, convert them into a common model and format, and finally merge the converted lexicons.


For example, all verbal lemmas that were not covered by ADESSE or SRG received the following subcategorization frame: <Suj:sn|cln,Obj:(sn|cla)> (transitive verb with optional direct object).

the application of semiautomatic techniques to extend and correct a lexicon, as described in [10], should help us fixing this aspect.


A well-known situation for a tagger is to consider an unknown proper noun as a common noun.

the scope of the process span an entire corpora and not only one sentence.


In addition, parallel progress has been made in machine translation systems and spoken dialog systems as well [11], [12], [13].

even if a lot has been achieved so far, a whole lot more is still to be achieved, which is upon the future generation high-computing technology [14].


The classifier at the root of generating the desired output (dependency graphs and semantic role assignments) is Classifier 1, which predicts the semantic and syntactic dependencies that hold between two tokens.

the classifier predicts incorrect dependencies to a certain degree, and does not produce a graph in wich all tokens have at least a syntactic head.


Because syntactic and semantic dependencies have been linked, the tree contains also semantic dependencies.

the tree is missing the semantic dependencies predicted by Classifier 1 that do not have a syntactic dependency part.


Meanwhile the polynomial kernel method is effective in many natural language processing tasks, since it takes into account the combination of features.

it is computationally difficult to simultaneously use both the structured output learning and the kernel method.


For calculating the kernel, we must regenerate the feature vector from x and y, or cache the kernel values.

it is impractical to hold all the N 2 m feature vectors.


The mere possibility of doing this exhaustively is a much debated issue, because even among humans, it is very difficult to agree on a set of senses for any given word.

if we consider it possible to obtain a list of the senses, even if it is incomplete, then it can be acquired from human knowledge (as one can find in any good dictionary), or from automatic discovery, a task which is generally called Word Sense Induction.


This work was partly realized as part of the Quaero Programme, funded by OSEO, French State agency for innovation.

with other NLP tasks, only few and limited evaluation challenges have been carried out for terminology acquisition.


A lot of terminological tools have been developed since the early research works of the 90s and many content management companies now rely on them [20].

despite the progress made, it remains difficult to get a clear idea of the maturity of computational terminology and to compare the proposed approaches.


The fact that terminological tools are often assisting tools for terminologists and application dependent, the relativity of any standard terminology, the complexity of terminological resources make the evaluation difficult in computational terminology.

this paper proposes an evaluation protocol and the associated metrics that take into account terminological specificity and nevertheless enable to set up comparative evaluations in a simple way.


In [2], they made a better table-of-contents with a supervised learning method which accounts for a number of features at word level and word sequence level.

in their experiments, a large amount of titles in the result table-ofcontents were not related to the content of the corresponding segments.


We used the content and the table-of-contents of the textbook "Introduction to Algorithm" [8] as the corpus for the learning model which is the same as [2].

we used Charniak's tool 2 to get part-ofspeech information for words in the book.


In this research, the Brown word clustering algorithm uses only correlation of words at the sentence level.

to generate a title for a document, we need the correlation of words at the document level.


Second, we notice that accuracy rates for some classes are extremely high, which is mainly the case of classes of well-defined semantics (for example, University policies determine that a 5.0 grade should be considered average, and this kind of knowledge was taken into account by the domain expert when writing the reports.)

this is not the case when we consider more subjective classes such as, e.g., progresso, which describes the student's performance curve throughout the term (e.g., rising, falling, U-shaped etc.


Most of the well-known language model smoothing techniques (see for example [12]) can be imported to the SMT field and specifically to the PBT framework, as it is shown in [6].

pBT and the generation of phrase-alignments differ in a key aspect.


Establishing relationships among languages which have been in contact for a long time has been a topic of interest in historical linguistics [6].

this topic has been much less explored in the computational linguistics community.


Unfortunately, it is not divided into proper training and testing subsets, and thus we were forced to use a 10-fold cross-validation in order to be directly comparable to previous publications.

there are some complications since we further want to be able to optimize some parameters such as the value of q.


Fully removing the first few words in the documents yields a decrease when done for the first 10% of the words.

there are some benefits of having less noise, e.g., when removing all words after the first 10% up until 50%.


Results from preliminary experiments (Section 5) show that GI provides correct information concerning polarity of non literal senses.

sentiWordNet [4] is a resource for opinion mining assigning every synset in WordNet three scores, which represent the probability for a sense of a word to be used in a positive, negative or objective manner.


Moreover, after experimental evaluation with various WordNet features in order to detect sense level polarity for non literal senses, we decided that the best representative combination in judging the polarity for non literal senses is the combination which consists of synsets and GlossExamples (GES) of non literal senses.

sen-tiWordNet uses synsets and the whole gloss of every WordNet sense, in order to judge sense level polarity.


For metaphors, the GV based WSD method is better than the one using baseline WSD, within the 1% statistical error threshold (pvalue 0.01).

for expanded senses we cannot support within 5% statistical error that GV based WSD performs better than baseline (p-value 0.20).


It is a direct attempt at capturing the paradigmatic relations between words.

the clustering can not find associations between words that appear further apart within specific documents.


Sure, we can extend the context to include the entire paragraph or the document it came from.

even in such situations the interpretation may remain indeterminate.


As mentioned in the previous section, this relation belongs to the following contingency set: {Origin-Entity, Purpose, Measure, Content-Container}.

it does not interact in the same way with each of the relations in the set.


Therefore, there is a rising interest in developing automatic or semi-automatic methods and techniques for building language resources with minimal human intervention.

automatic methods usually involve a large set of parameters, whose impact on final results is difficult to assess, and thus to optimize.


Psychological verbs do not have a prototypical syntactic structure and they usually convey a plurality of meanings, which can only be disambiguated in context.

declarative verbs are less ambiguous and the syntactic structure where they occur is better defined.


For example, words which denote material objects (house, train) have senses dominated by the referents.

words which denote abstract concepts as sensations, feelings and emotions keep more sound semantics in their forms (anger, joy, agitation).


For each representation, the classification results are close across the data sets.

there is a remarkable difference between the Electronics data and the three other sets.


Another tagger has been developed using the same data but with a different cost parameter (0.3).

no improvement in performance has been observed.


Most of the previous work on DA assignation assumed the correct segmentation of the dialogue turns.

in a real situation, the only data that are available are the dialogue turns, and the segmentation is not available.


The algorithm was evaluated on the same subset of Europarl corpus that we used.

they apply an evaluation framework that is different from that of [12] presented in the previous section.


In general, the two results reflect the different goals of the two approaches: [1] are interested in investigating and adopting unsupervised techniques with poor semantic and syntactic information to automatically annotate a large scale (but noisy) training set and exploit it for semantic role labelling.

we are interested in developing annotated resources with nearly manual quality, so we consider particularly important FE transfer precision.


We propose a third approach based on 3 main ideas: 1) we think that it is preferable to evaluate separately targets and frame elements, because of the different nature of the two tasks: target transfer is more influenced by word alignment quality and is generally more straightforward than FE projection.

the latter requires a different strategy because it involves selection procedures at chunk or constituent level.


Evaluation results show that the translation effort to produce the corpus is repaid by the re-markable reduction of correction work.

we are aware that transferring only one sentence per frame and controlling translation allows to cover only one of the possible valence patterns of the frame.


The semantic search architecture, described in the previous section, has been developed on the basis of a manually created ontology and a corpus in the computing domain.

in order for the LT4eL eLearning prototype to develop into a real life application, it is necessary to create new domain ontologies and more general lexicons.


This list of extracted terms could still support a human expert in the completion of the ontology.The advantage of this list is that it contains terms that are provided with WordNet synset IDs with them.

en-hancing the kick-off ontology with these terms would still be a manual task.


To conclude: both Watson and Prompt are tools that provide valuable support to the task of enriching an ontology with new concepts.

the oneby-one additions to the ontology which Watson supports leaves the ontology builder still with a significant amount of work.


In the LT4eL project, these lexicons were created manually.

another possibilty, at least for Dutch, is to employ a lexical resource recently developed, that is Cornetto [14].


The Cornetto is a lexicon for an open domain from which we need to filter the relevant terms from the music domain.

wordNet has been labeled with labels from the Dewey Decimal Classification which resulted in wordNet Domains.


, n, hyponym(NP i , NP 0 ) there seems to be much fewer patterns, if any, to follow for using synonyms in free-text writing.

in the composition of dictionary definitions, there is usually much more regularity in terms of how synonyms of the word being defined should and would appear.


Morphological filtering can reduce the number of pronoun-markable pairs to about half the size of the original set.

there are exceptions to this agreement restriction.


where the plural personal pronoun sie is coreferent to Finnland (Finland) and to die Finnen (the Finns).

finnland is singular, thus it is not compatible in number with the plural pronoun.


The treebank provides full coreference chains.

we follow standard practice in anaphora resolution and consider the closest coreferent markable as the sole antecedent of a pronoun.


We are aware that the feature set that proved optimal for the memory-based classifier may not guarantee optimal performance for other classifiers.

if we had optimized the feature sets for the different classifiers, we would have introduced another free variable into the experiment, and we would not have been able to distinguish differences based on sampling from differences based on the feature sets.


For example, longer noun phrases like "congress party spokesman" are usually more specific and thus more informative to the reader than shorter noun phrases like "congress party".

due to reading and writing economy, specific terms are usually not often repeated in a document [1].


Turney [23] augments Kea with a feature set based on statistical word association to ensure that the returned keyphrase set is coherent.

this assumption might not hold if a document covers different topics.


Agreement between all 4 judges is considerably lower for Includes and PartOf.

given the inherent subjectivity of the task, we treat an agreement of 3 out of 4 judges as valid for accepting a match.


A potential refinement of the PartOf heuristic would be to match only extracted keyphrases whose head noun matches the head of the gold keyphrase.

only 52% of such cases (66 out of 128) were accepted by at least 3 judges.


Unfortunately, doing so reduces the portability of the method.

we believe that the results obtained are still very valuable, because they show that a considerable improvement over the baseline can be obtained even with a domain-independent, off-the-shelf similarity function.


For example, we can first run a topic classification filter to predict whether or not a document is likely to contain a specific type of event.

because of the limited precision of a simple classifier such as a bag-of-words MaxEnt classifier (for Attack events, the precision is around 69% in ACE data), using it as a pre-filter will lead to event recall or precision errors.


We might expect supervised topic features to outperform unsupervised topic features, when the distribution of training and testing data are the same, because its correlation to event type is clearer and explicit.

this turns out not to be true in our experiment (Table 2): the unsupervised features work better than the supervised features.


The reason is that there are a lot of metaphors that do not represent true Die events, like "dying nation", "dying business", "dying regime".

when connected to the latent topic features, we know that for some topics, we can confidently tag it as a Die event.


Therefore the adaptation might be limited to substituting the seed examples.

different domains/corpora exhibit rather different properties of their learning/extraction data with respect to the learning algorithm.


Several rules of similar type are the reason for the low precision in the entertainer and the politician domains.

all three domains share the good rule: The extraction results from the merged corpus are comparable to the entertainment domain: low precision and high gain of instances.


The use of a wider context feature window and surrounding verbs improves the overall f-measure scores for inter-sentential relations, substantially improving recall while slightly degrading precision.

for the intra-sentential case adding context and verb features either maintains or degrades performance.


Natural language processing tasks often rely on part-of-speech (POS) tagging as a preprocessing step.

it is not clear how the absence of any part-of-speech tagger should hamper the development of other natural language processing tools.


They showed a significant improvement of the use of unsupervised pseudo part-of-speech tags over the baseline that discarded any POS information.

their experiments covered several tasks and were not focused on shallow parsing.


The authors report that vocabulary is the main difference between the domains.

sCL can only be applied to feature-based discriminative learning methods.


TnT, in contrast, suffers a non-significant decrease in performance.

tnt's baseline results are significantly higher than the two other taggers'.


The reason for requiring all three POS taggers to agree on full sentences is that the selected sentences will be reliable.

the method also has the drawback that only a rather small number of sentences fulfill this criterion.


From the error analysis, we can conclude that the added words do not correspond to the words that are needed in the test domain, which means that the HCRC map task corpus data are not similar enough to the CReST data.

we can also conclude that even if there were a larger overlap, there is a high chance that those words would be mistagged by the ensemble of taggers so that adding the new words would result in a deterioration of the performance.


Such words must be treated in the tagger's module for handling unknown words, which is often based on suffix information extracted from infrequent words in the training set.

many of the unknown words in the CReST corpus are colloquial words and thus do not show the same morphological characteristics as words in the training set.


The improvement from adding in-domain trigrams is due to more accurate transition probabilities.

the lexical additions from the in-domain data were detrimental.


The active learning strategy of adding user-defined lexical information for difficult unknown words improves this accuracy to 89.15%.

this accuracy is still far below an in-domain accuracy, which reaches 95.66%.


We may be able to improve results for those two taggers if we optimize the feature set for the target domain.

this means modifying the implementation of the taggers since the feature extraction is not modular.


In (Rupnik et al., 2008), a dictionary derived from training is essentially extended by using a backup lexicon extracted from a large corpus (which is different from the training corpus).

we use a morphological database to extend a tagger's dictionary, but use a corpus for deriving frequency information for part of the dictionary entries.


When a word cannot be fully disambiguated, this enables IceTagger to select the most frequent tag (the first tag) in the tag profile for the word.

when frequency information is missing, as is the case for the BÍN data, the first tag of the remaining tags in the tag profile may or may not be the most frequent tag.


The dependency parser performs equally badly when trained on the Penn Treebank (40.6 LAS).

when it is trained on in-domain data, it reaches an LAS of 70.5, which corroborates the assumption that TiMBL performs well with small data sets.


For each MICA elementary tree t j and XTAG elementary tree t i the observation probability matrix (B = [b i, j ]) also contains the probability P(t j |t i ).

the alternate model, M-2, tries to model the relation between the grammars with respect to the existence gap between them.


For example the rule that a common noun can be modified by an adjective is independent of any language and hence is defined in the abstract syntax, e.g.

: the way this rule is implemented may vary from one language to another; as each language may have different word order and/or agreement rules.


With minor required adjustments, we have reused morphology and a subset of that lexicon, as a test lexicon of about 450 words for our grammar implementation.

the morphological details are beyond the scope of this paper and we refer to (Humayoun and Ranta, 2010) for more details on Punjabi morphology.


Consequently, the current grammar does not cover all aspects of Punjabi.

this does not put any limitation on the extension of a language resource.


If we look at the context of these texts, the text in Figure 1 is a short description about Bacteria.

the text in Figure 2 talks about the fundamental difference between Bacteria and Fungi.


As a consequence, it is more likely that Moses learns specific vocabulary from the series and that translations look more similar to those in the reference set.

when BLEU is computed with multiple references, even though the translations from all systems may differ from what was originally expected (ref 0 ), they can still be valid alternative translations that often match the choices made by other translators (ref 1−17 ).